
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt


# Import the data set
data = pd.read_excel('D:\\simpli learn PG Pragram\\PG Data Analylists\\Live Lecture Recording\\Data Analyst Capstone\\Identifying and Recommending Best Restaurants\\datasets_marketing\\data.xlsx')


data.head()


cc = pd.read_excel('D:\\simpli learn PG Pragram\\PG Data Analylists\\Live Lecture Recording\\Data Analyst Capstone\\Identifying and Recommending Best Restaurants\\datasets_marketing\\Country-Code.xlsx')


df_rest = pd.merge(data,cc,on='Country Code',how='left')
df_rest.head()


df_rest.columns = df_rest.columns.str.replace(' ','_')
df_rest.columns

Index(['Restaurant_ID', 'Restaurant_Name', 'Country_Code', 'City', 'Address',
       'Locality', 'Locality_Verbose', 'Longitude', 'Latitude', 'Cuisines',
       'Average_Cost_for_two', 'Currency', 'Has_Table_booking',
       'Has_Online_delivery', 'Price_range', 'Aggregate_rating',
       'Rating_color', 'Rating_text', 'Votes', 'Country'],
      dtype='object')


df_rest.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 9551 entries, 0 to 9550
Data columns (total 20 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   Restaurant_ID         9551 non-null   int64  
 1   Restaurant_Name       9550 non-null   object 
 2   Country_Code          9551 non-null   int64  
 3   City                  9551 non-null   object 
 4   Address               9551 non-null   object 
 5   Locality              9551 non-null   object 
 6   Locality_Verbose      9551 non-null   object 
 7   Longitude             9551 non-null   float64
 8   Latitude              9551 non-null   float64
 9   Cuisines              9542 non-null   object 
 10  Average_Cost_for_two  9551 non-null   int64  
 11  Currency              9551 non-null   object 
 12  Has_Table_booking     9551 non-null   object 
 13  Has_Online_delivery   9551 non-null   object 
 14  Price_range           9551 non-null   int64  
 15  Aggregate_rating      9551 non-null   float64
 16  Rating_color          9551 non-null   object 
 17  Rating_text           9551 non-null   object 
 18  Votes                 9551 non-null   int64  
 19  Country               9551 non-null   object 
dtypes: float64(3), int64(5), object(12)
memory usage: 1.5+ MB


#total number of null entries per column
df_rest.isnull().sum()

Restaurant_ID           0
Restaurant_Name         1
Country_Code            0
City                    0
Address                 0
Locality                0
Locality_Verbose        0
Longitude               0
Latitude                0
Cuisines                9
Average_Cost_for_two    0
Currency                0
Has_Table_booking       0
Has_Online_delivery     0
Price_range             0
Aggregate_rating        0
Rating_color            0
Rating_text             0
Votes                   0
Country                 0
dtype: int64


df_rest[df_rest['Restaurant_Name'].isnull()]


#Since the restaurant name is missing, we dropped the record and reset the index.
df_rest.dropna(axis=0,subset=['Restaurant_Name'],inplace=True)
df_rest.reset_index(drop=True,inplace=True)
df_rest[df_rest['Cuisines'].isnull()]


#Since there were only 9 records without cuisines, we have replace the null values with Others.
df_rest['Cuisines'].fillna('Others',inplace=True)


df_rest.isnull().sum()
df_rest.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9550 entries, 0 to 9549
Data columns (total 20 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   Restaurant_ID         9550 non-null   int64  
 1   Restaurant_Name       9550 non-null   object 
 2   Country_Code          9550 non-null   int64  
 3   City                  9550 non-null   object 
 4   Address               9550 non-null   object 
 5   Locality              9550 non-null   object 
 6   Locality_Verbose      9550 non-null   object 
 7   Longitude             9550 non-null   float64
 8   Latitude              9550 non-null   float64
 9   Cuisines              9550 non-null   object 
 10  Average_Cost_for_two  9550 non-null   int64  
 11  Currency              9550 non-null   object 
 12  Has_Table_booking     9550 non-null   object 
 13  Has_Online_delivery   9550 non-null   object 
 14  Price_range           9550 non-null   int64  
 15  Aggregate_rating      9550 non-null   float64
 16  Rating_color          9550 non-null   object 
 17  Rating_text           9550 non-null   object 
 18  Votes                 9550 non-null   int64  
 19  Country               9550 non-null   object 
dtypes: float64(3), int64(5), object(12)
memory usage: 1.5+ MB


#We observe that India has then highest number of restaurants with 8651 restaurants and USA is number 2 with 434 restaurants
cntry_dist = df_rest.groupby(['Country_Code','Country']).agg( Count = ('Restaurant_ID','count'))
cntry_dist.sort_values(by='Count',ascending=False)


cntry_dist.plot(kind='barh')

<AxesSubplot:ylabel='Country_Code,Country'>


#city with max restaurant has count = 5473
#city with min restaurant has count = 1
city_dist = df_rest.groupby(['Country','City']).agg(Count = ('Restaurant_ID','count'))
city_dist.describe()


city_dist.sort_values(by='Count',ascending=False)
# we see that new Delhi has the maximum restaurant with 5473
# we observe that multiple cities have only one restaurant.


min_cnt_rest = city_dist[city_dist['Count']==1]
min_cnt_rest.info()
min_cnt_rest
#There are 46 cities in 7 different countries with 1 restaurants

<class 'pandas.core.frame.DataFrame'>
MultiIndex: 46 entries, ('Australia', 'Armidale') to ('United States', 'Winchester Bay')
Data columns (total 1 columns):
 #   Column  Non-Null Count  Dtype
---  ------  --------------  -----
 0   Count   46 non-null     int64
dtypes: int64(1)
memory usage: 1.8+ KB


max_rate = df_rest.sort_values(by='Aggregate_rating',ascending=False).groupby(['Country','City'],as_index=False).first()
#highest rating restaurants

min_rate = df_rest.sort_values(by='Aggregate_rating',ascending=False).groupby(['Country','City'],as_index=False).last()
#lowest rating restaurants

df_max=max_rate[['Country','City','Restaurant_Name','Aggregate_rating']] #new dataframe created for high rated restaurants

df_min=min_rate[['Country','City','Restaurant_Name','Aggregate_rating']] #new dataframe created for low rated restaurants

rating_rest=df_max.merge(df_min,left_on='City',right_on='City',how='inner') #merge into single dataframe


rating_rest


rating_rest.drop(columns='Country_y',axis=1,inplace=True)
rating_rest.columns = ['Country','City','Highest Rated Restaurant','Rating Max','Lowest Rated Restaurant','Rating Min']
rating_rest


#since India and USA has the most number of restaurants, 
#we will try to see the distribution of restaurants ratings for these two countries.

from plotly.offline import download_plotlyjs, init_notebook_mode, iplot
from plotly.graph_objs import * 
init_notebook_mode()
import plotly.graph_objs as go #importing plotly or graphs


rating_rest_city_india=rating_rest[rating_rest['Country']=='India'] #storing the dataframe only for country 'India'
rating_rest_city_india #In India
city=rating_rest_city_india['City'].tolist()#converting the series to list 
rate_max=rating_rest_city_india['Rating Max'].tolist()#converting the series to list
rate_min=rating_rest_city_india['Rating Min'].tolist()#converting the series to list
rest_name_high=rating_rest_city_india['Highest Rated Restaurant'].tolist()#converting the series to list
rest_name_low=rating_rest_city_india['Lowest Rated Restaurant'].tolist()


stack0 = go.Bar( # GroupBarChart 1 (Highest Rated Resturant)
    x=city,#x axis label
    y=rate_max,# y axis label
    text=rest_name_high,# the value of the restaurant
    name='Highest Rated Restaurant',
     marker=dict(
        color='rgb(76,153,0)', #color of the bar graph's marker
        line=dict(
            color='rgb(76,153,0)', #color of the bar graph's line
            width=1.5, #width of the bar graph
        )
    ),
    opacity=1.0
)
stack1 = go.Bar( # GroupBarChart 2 (Lowest Rated Resturant)
    x=city,
    y=rate_min,
    text=rest_name_low,
    name='Lowest Rated Restaurant',
     marker=dict(
        color='rgb(255,0,0)',#color of the bar graph's marker
        line=dict(
            color='rgb(255,0,0)',#color of the bar graph's line
            width=1.5, #width of the bar graph
        )
    ),
    opacity=1.0
)

data = [stack0,stack1]
layout = go.Layout(
    legend=dict( #the layout of the graph( beautification)
        x=0,
        y=1,
        traceorder='normal',
        font=dict(
            family='sans-serif',
            size=12,
            color='#000'
        ),
        bgcolor='#E2E2E2',
        bordercolor='#FFFFFF',
        borderwidth=2
    ),
    autosize=False,
    width=1000, # size of the graph
    height=450,
    barmode='group',
    title="Graph 1.1: Restaurants rating of India <br>\
    <i>hover with cursor to see restaurant's name</i>", #title of the graph
    plot_bgcolor='rgba(245, 246, 249, 1)',
    xaxis=dict(tickangle=-45,title= 'City of India'), #making the graphs label inclined at 45 deg
    yaxis= {'title': 'Rating(scale of 5)'} #label of y-axis
)
fig = go.Figure(data=data, layout=layout) #plotting the graph
iplot(fig, filename='style-barbar')


#perform the same steps as above for Country='United States'
rating_rest_city_usa = rating_rest[rating_rest['Country']=='United States']
rating_rest_city_usa
cityu = rating_rest_city_usa['City'].tolist()
rate_maxu = rating_rest_city_usa['Rating Max'].tolist()
rate_minu = rating_rest_city_usa['Rating Min'].tolist()
rest_name_highu = rating_rest_city_usa['Highest Rated Restaurant'].tolist()
rest_name_lowu = rating_rest_city_usa['Lowest Rated Restaurant'].tolist()


stack0 = go.Bar( # GroupBarChart 1 (Highest Rated Resturant)
    x=cityu,#x axis label
    y=rate_maxu,# y axis label
    text=rest_name_highu,# the value of the restaurant
    name='Highest Rated Restaurant',
     marker=dict(
        color='rgb(76,153,0)', #color of the bar graph's marker
        line=dict(
            color='rgb(76,153,0)', #color of the bar graph's line
            width=1.5, #width of the bar graph
        )
    ),
    opacity=1.0
)
stack1 = go.Bar( # GroupBarChart 2 (Lowest Rated Resturant)
    x=cityu,
    y=rate_minu,
    text=rest_name_lowu,
    name='Lowest Rated Restaurant',
     marker=dict(
        color='rgb(255,0,0)',#color of the bar graph's marker
        line=dict(
            color='rgb(255,0,0)',#color of the bar graph's line
            width=1.5, #width of the bar graph
        )
    ),
    opacity=1.0
)

data = [stack0,stack1]
layout = go.Layout(
    legend=dict( #the layout of the graph( beautification)
        x=0,
        y=1,
        traceorder='normal',
        font=dict(
            family='sans-serif',
            size=12,
            color='#000'
        ),
        bgcolor='#E2E2E2',
        bordercolor='#FFFFFF',
        borderwidth=2
    ),
    autosize=False,
    width=1000, # size of the graph
    height=450,
    barmode='group',
    title="Graph 1.1: Restaurants rating of USA <br>\
    <i>hover with cursor to see restaurant's name</i>", #title of the graph
    plot_bgcolor='rgba(245, 246, 249, 1)',
    xaxis=dict(tickangle=-45,title= 'City of USA'), #making the graphs label inclined at 45 deg
    yaxis= {'title': 'Rating(scale of 5)'} #label of y-axis
)
fig = go.Figure(data=data, layout=layout) #plotting the graph
iplot(fig, filename='style-barbar')


df_rest1 = df_rest.copy()
df_rest1.columns

Index(['Restaurant_ID', 'Restaurant_Name', 'Country_Code', 'City', 'Address',
       'Locality', 'Locality_Verbose', 'Longitude', 'Latitude', 'Cuisines',
       'Average_Cost_for_two', 'Currency', 'Has_Table_booking',
       'Has_Online_delivery', 'Price_range', 'Aggregate_rating',
       'Rating_color', 'Rating_text', 'Votes', 'Country'],
      dtype='object')


dummy = ['Has_Table_booking','Has_Online_delivery']
df_rest1 = pd.get_dummies(df_rest1,columns=dummy,drop_first=True)
df_rest1.head()
# 0 indicates 'NO'
# 1 indicates 'YES'


#Ration between restaurants allowing table booking and those which dont
table_booking = df_rest1[df_rest1['Has_Table_booking_Yes']==1]['Restaurant_ID'].count()
table_nbooking =df_rest1[df_rest1['Has_Table_booking_Yes']==0]['Restaurant_ID'].count()
print('Ratio between restaurants that allow table booking vs. those that do not allow table booking: ',
      round((table_booking/table_nbooking),2))

Ratio between restaurants that allow table booking vs. those that do not allow table booking:  0.14


print(table_booking,table_nbooking)

1158 8392


#Pie chart to show percentage of restaurants which allow table booking and those which don't
labels = 'Table Booking', 'No Table Booking'
sizes = [table_booking,table_nbooking]
explode = (0.1, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')

fig1, ax1 = plt.subplots(figsize=(9,9))
ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',shadow=True, startangle=90)
ax1.set_title("Table Booking vs No Table Booking")
ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.

plt.show()


#Percentage of restaurant that has online delivery
rest_od = df_rest1[df_rest1['Has_Online_delivery_Yes'] == 1]['Restaurant_ID'].count()
rest_nod = df_rest1[df_rest1['Has_Online_delivery_Yes'] == 0]['Restaurant_ID'].count()
print('Percentage of restaurants providing online delivery : {} %'.format((round(rest_od/len(df_rest1),3)*100)))

Percentage of restaurants providing online delivery : 25.7 %


#pie chart to show percentages of restaurants allowing online delivery vs those which do not have online delivery
labels = 'Online Delivery','No Online Delivery'
size = [rest_od,rest_nod]
explode = (0.1,0)
fig1,ax1 = plt.subplots(figsize=(9,9))
ax1.pie(size,explode=explode,labels=labels,autopct='%1.1f%%',shadow=True,startangle=90)
ax1.set_title("Online Delivery vs No Online Delivery")
ax1.axis('equal')
plt.show()


rest_deliver = df_rest1[df_rest1['Has_Table_booking_Yes'] == 1]['Votes'].sum()
rest_ndeliver = df_rest1[df_rest1['Has_Table_booking_Yes'] == 0]['Votes'].sum()
print('Difference in number of votes for restaurants that deliver and dont deliver: ',abs((rest_deliver - rest_ndeliver)))

Difference in number of votes for restaurants that deliver and dont deliver:  680082


labels = 'Online Delivery','No Online Delivery'
size = [rest_ndeliver,rest_deliver]
explode = (0,0.1)
fig1,ax1 = plt.subplots(figsize=(9,9))
ax1.pie(size,explode=explode,labels=labels,autopct='%1.1f%%',shadow=True,startangle=90)
ax1.set_title("Votes: Online Delivery vs Votes:No Online Delivery")
ax1.axis('equal')
plt.show()
#out of the total votes about 27.3% votes were given to restaurants that dont have online delivery option
#out of the total votes about 72.7% votes were given to restaurants that dont have online delivery option
#This clearly shows that restaurants that have online delivery are more likely to get a vote(feedback)


df_rest.columns
cuisines = df_rest['Cuisines'].apply(lambda x: pd.Series(x.split(',')))


cuisines.columns = ['Cuisine_1','Cuisine_2','Cuisine_3','Cuisine_4','Cuisine_5','Cuisine_6','Cuisine_7','Cuisine_8']
cuisines.tail()


df_cuisines = pd.concat([df_rest,cuisines],axis=1)
df_cuisines.head()


cuisine_loc = pd.DataFrame(df_cuisines[['Country','City','Locality_Verbose','Cuisine_1','Cuisine_2','Cuisine_3',
                                        'Cuisine_4','Cuisine_5','Cuisine_6','Cuisine_7','Cuisine_8']])


cuisine_loc_stack=pd.DataFrame(cuisine_loc.stack()) #stacking the columns 
cuisine_loc.head()


keys = [c for c in cuisine_loc  if c.startswith('Cuisine')]
a=pd.melt(cuisine_loc, id_vars='Locality_Verbose', value_vars=keys, value_name='Cuisines') 
#melting the stack into one row
max_rate=pd.DataFrame(a.groupby(by=['Locality_Verbose','variable','Cuisines']).size().reset_index())
#find the highest restuarant in the city
max_rate
del max_rate['variable']
max_rate.columns=['Locality_Verbose','Cuisines','Count']
max_rate.head()


#find the highest restuarant in the city
loc=max_rate.sort_values('Count', ascending=False).groupby(by=['Locality_Verbose'],as_index=False).first()
loc.head()


rating_res=loc.merge(df_rest,left_on='Locality_Verbose',right_on='Locality_Verbose',how='inner') 
#inner join to merge the two dataframe
df=pd.DataFrame(rating_res[['Country','City','Locality_Verbose','Cuisines_x','Count']]) 
#making a dataframe of rating restaurant
country=rating_res.sort_values('Count', ascending=False).groupby(by=['Country'],as_index=False).first()
#grouping the data by country code
con=pd.DataFrame(country[['Country','City','Locality','Cuisines_x','Count']])
con.columns=['Country','City','Locality','Cuisines','Number of restaurants in the country']
#renaming the columns
con1=con.sort_values('Number of restaurants in the country', ascending=False) 
#sorting the restaurants on the basis of the number of restaurants in the country
con1[:10]
final_con=con1.drop(con1.index[[7,10]])


final_con


loc_list=final_con['City'] #converting the series to dataframe
a_list=loc_list.tolist()

cui_list=final_con['Cuisines']# converting the series to dataframe
b_list=cui_list.tolist()

count_list=final_con['Number of restaurants in the country']# converting the series to dataframe
c_list=count_list.tolist()


trace0 = go.Bar(# BarChart 1 (Popular cuisines of the country)
    x=b_list, #x axis label
    y=c_list, # y axis label
    text=loc_list, # location of the cuisine
    name='Popular Cuisine',
     marker=dict(
        color=['rgb(255,69,0)',
                'rgb(255,140,0)',
                'rgb(165,42,42)',
                'rgb(220,20,60)',
                'rgb(255,0,0)',
                'rgb(255,99,71)',
                'rgb(255,127,80)',
                'rgb(205,92,92)',
                'rgb(240,128,128)',
                'rgb(233,150,122)',
                'rgb(250,128,114)',
                'rgb(255,160,122)'],
        line=dict(
            color='rgb(255,0,0)',#color of the bar graph's line
            width=1.5, #width of the bar graph
        )
    ),
    opacity=1.0
)
data = [trace0] 
layout = go.Layout(

    legend=dict( #the layout of the graph( beautification)
        x=0,
        y=1,
        traceorder='normal',
        font=dict(
            family='sans-serif',
            size=12,
            color='#000'
        ),
        bgcolor='#E2E2E2',
        bordercolor='#FFFFFF',
        borderwidth=20,
    ),
    autosize=False,
    width=1000, # size of the graph
    height=450,
    margin=Margin(r=20, l=300,
                  b=75, t=125),
    title="Graph 2.1 : Most popular cuisines in the World<br>\
    <i>hover with cursor to see location in the country where they are most popular </i>", #title of the graph
    plot_bgcolor='rgba(245, 246, 249, 1)',
    xaxis=dict(tickangle=-45,title= '<br>Cuisine<br>',mirror=True,showticklabels=True), 
    #making the graphs label inclined at 45 deg
    yaxis= {'title': 'Number of restaurants offering<br> cuisine in the location'},#label of y-axis
)
fig = go.Figure(data=data, layout=layout)#plotting the graph
iplot(fig)

C:\Users\Rahul\anaconda3\lib\site-packages\plotly\graph_objs\_deprecations.py:405: DeprecationWarning:

plotly.graph_objs.Margin is deprecated.
Please replace it with one of the following more specific types
  - plotly.graph_objs.layout.Margin


rest_cuisine = pd.DataFrame(df_cuisines[['Restaurant_Name','City','Cuisine_1','Cuisine_2','Cuisine_3','Cuisine_4',
                                         'Cuisine_5','Cuisine_6','Cuisine_7','Cuisine_8']])
rest_cuisine_stack=pd.DataFrame(rest_cuisine.stack()) #stacking the columns 
rest_cuisine.head()


keys1 = [c for c in rest_cuisine  if c.startswith('Cuisine')]
b=pd.melt(rest_cuisine, id_vars='Restaurant_Name', value_vars=keys, value_name='Cuisines') 
#melting the stack into one row
max_rate1=pd.DataFrame(b.groupby(by=['Restaurant_Name','variable','Cuisines']).size().reset_index()) 
#find the highest restuarant in the city
max_rate1
del max_rate1['variable']
max_rate1.columns=['Restaurant_Name','Cuisines','Count']
max_rate1.head(20)


max_rate1.sort_values('Count',ascending=False)
#Cafe Coffee Day has the max number of cuisines and The least number of cuisines in a resaurant is 1.


rating = df_rest1[['Restaurant_ID','Restaurant_Name','Country','City','Aggregate_rating','Average_Cost_for_two','Votes','Price_range','Has_Table_booking_Yes','Has_Online_delivery_Yes']]


rating = rating.merge(max_rate1,left_on='Restaurant_Name',right_on='Restaurant_Name',how='left')
rating


sns.regplot(x='Count',y='Aggregate_rating',data=rating)
rating[["Count", "Aggregate_rating"]].corr()
#Number of cuisines is not a good factor to decide the rating of a restaurant


sns.regplot(x='Average_Cost_for_two',y='Aggregate_rating',data=rating)
rating[["Average_Cost_for_two", "Aggregate_rating"]].corr()
#Average cost for two is a weak positive factor to decide the rating of a restaurant


sns.regplot(x='Votes',y='Aggregate_rating',data=rating)
rating[['Votes','Aggregate_rating']].corr()
##Average cost for two can be a factor to decide the rating of a restaurant


abc = df_rest1[df_rest1['Has_Online_delivery_Yes'] == 1]['Aggregate_rating'].mean()
xyz = df_rest1[df_rest1['Has_Online_delivery_Yes'] == 0]['Aggregate_rating'].mean()
sns.regplot(x='Price_range',y='Aggregate_rating',data=rating)
rating[['Price_range','Aggregate_rating']].corr()
##Price range can be a factor to decide the rating of a restaurant


sns.regplot(x='Has_Table_booking_Yes',y='Aggregate_rating',data=rating)
rating[['Has_Table_booking_Yes','Aggregate_rating']].corr()
##Table booking can be a factor to decide the rating of a restaurant

	Restaurant ID	Restaurant Name	Country Code	City	Address	Locality	Locality Verbose	Longitude	Latitude	Cuisines	Average Cost for two	Currency	Has Table booking	Has Online delivery	Price range	Aggregate rating	Rating color	Rating text	Votes
0	7402935	Skye	94	Jakarta	Menara BCA, Lantai 56, Jl. MH. Thamrin, Thamri...	Grand Indonesia Mall, Thamrin	Grand Indonesia Mall, Thamrin, Jakarta	106.821999	-6.196778	Italian, Continental	800000	Indonesian Rupiah(IDR)	No	No	3	4.1	Green	Very Good	1498
1	7410290	Satoo - Hotel Shangri-La	94	Jakarta	Hotel Shangri-La, Jl. Jend. Sudirman	Hotel Shangri-La, Sudirman	Hotel Shangri-La, Sudirman, Jakarta	106.818961	-6.203292	Asian, Indonesian, Western	800000	Indonesian Rupiah(IDR)	No	No	3	4.6	Dark Green	Excellent	873
2	7420899	Sushi Masa	94	Jakarta	Jl. Tuna Raya No. 5, Penjaringan	Penjaringan	Penjaringan, Jakarta	106.800144	-6.101298	Sushi, Japanese	500000	Indonesian Rupiah(IDR)	No	No	3	4.9	Dark Green	Excellent	605
3	7421967	3 Wise Monkeys	94	Jakarta	Jl. Suryo No. 26, Senopati, Jakarta	Senopati	Senopati, Jakarta	106.813400	-6.235241	Japanese	450000	Indonesian Rupiah(IDR)	No	No	3	4.2	Green	Very Good	395
4	7422489	Avec Moi Restaurant and Bar	94	Jakarta	Gedung PIC, Jl. Teluk Betung 43, Thamrin, Jakarta	Thamrin	Thamrin, Jakarta	106.821023	-6.196270	French, Western	350000	Indonesian Rupiah(IDR)	No	No	3	4.3	Green	Very Good	243

	Restaurant ID	Restaurant Name	Country Code	City	Address	Locality	Locality Verbose	Longitude	Latitude	Cuisines	Average Cost for two	Currency	Has Table booking	Has Online delivery	Price range	Aggregate rating	Rating color	Rating text	Votes	Country
0	7402935	Skye	94	Jakarta	Menara BCA, Lantai 56, Jl. MH. Thamrin, Thamri...	Grand Indonesia Mall, Thamrin	Grand Indonesia Mall, Thamrin, Jakarta	106.821999	-6.196778	Italian, Continental	800000	Indonesian Rupiah(IDR)	No	No	3	4.1	Green	Very Good	1498	Indonesia
1	7410290	Satoo - Hotel Shangri-La	94	Jakarta	Hotel Shangri-La, Jl. Jend. Sudirman	Hotel Shangri-La, Sudirman	Hotel Shangri-La, Sudirman, Jakarta	106.818961	-6.203292	Asian, Indonesian, Western	800000	Indonesian Rupiah(IDR)	No	No	3	4.6	Dark Green	Excellent	873	Indonesia
2	7420899	Sushi Masa	94	Jakarta	Jl. Tuna Raya No. 5, Penjaringan	Penjaringan	Penjaringan, Jakarta	106.800144	-6.101298	Sushi, Japanese	500000	Indonesian Rupiah(IDR)	No	No	3	4.9	Dark Green	Excellent	605	Indonesia
3	7421967	3 Wise Monkeys	94	Jakarta	Jl. Suryo No. 26, Senopati, Jakarta	Senopati	Senopati, Jakarta	106.813400	-6.235241	Japanese	450000	Indonesian Rupiah(IDR)	No	No	3	4.2	Green	Very Good	395	Indonesia
4	7422489	Avec Moi Restaurant and Bar	94	Jakarta	Gedung PIC, Jl. Teluk Betung 43, Thamrin, Jakarta	Thamrin	Thamrin, Jakarta	106.821023	-6.196270	French, Western	350000	Indonesian Rupiah(IDR)	No	No	3	4.3	Green	Very Good	243	Indonesia

	Restaurant_ID	Restaurant_Name	Country_Code	City	Address	Locality	Locality_Verbose	Longitude	Latitude	Cuisines	Average_Cost_for_two	Currency	Has_Table_booking	Has_Online_delivery	Price_range	Aggregate_rating	Rating_color	Rating_text	Votes	Country
9082	17374552	Corkscrew Cafe	216	Gainesville	51 W Main St, Dahlonega, GA 30533	Dahlonega	Dahlonega, Gainesville	-83.985800	34.531800	NaN	40	Dollar($)	No	No	3	3.9	Yellow	Good	209	United States
9085	17501439	Dovetail	216	Macon	543 Cherry St, Macon, GA 31201	Macon	Macon, Macon	-83.627979	32.836410	NaN	40	Dollar($)	No	No	3	3.8	Yellow	Good	102	United States
9093	17059060	Hillstone	216	Orlando	215 South Orlando Avenue, Winter Park, FL 32789	Winter Park	Winter Park, Orlando	-81.365260	28.596682	NaN	40	Dollar($)	No	No	3	4.4	Green	Very Good	1158	United States
9405	17284158	Jimmie's Hot Dogs	216	Albany	204 S Jackson St, Albany, GA 31701	Albany	Albany, Albany	-84.153400	31.575100	NaN	10	Dollar($)	No	No	1	3.9	Yellow	Good	160	United States
9493	17142698	Leonard's Bakery	216	Rest of Hawaii	933 Kapahulu Ave, Honolulu, HI 96816	Kaimuki	Kaimuki, Rest of Hawaii	-157.813432	21.284586	NaN	10	Dollar($)	No	No	1	4.7	Dark Green	Excellent	707	United States
9503	17616465	Tybee Island Social Club	216	Savannah	1311 Butler Ave, Tybee Island, GA 31328	Tybee Island	Tybee Island, Savannah	-80.848297	31.995810	NaN	10	Dollar($)	No	No	1	3.9	Yellow	Good	309	United States
9532	17284105	Cookie Shoppe	216	Albany	115 N Jackson St, Albany, GA 31701	Albany	Albany, Albany	-84.154000	31.577200	NaN	0	Dollar($)	No	No	1	3.4	Orange	Average	34	United States
9534	17284211	Pearly's Famous Country Cookng	216	Albany	814 N Slappey Blvd, Albany, GA 31701	Albany	Albany, Albany	-84.175900	31.588200	NaN	0	Dollar($)	No	No	1	3.4	Orange	Average	36	United States
9538	17606621	HI Lite Bar & Lounge	216	Miller	109 N Broadway Ave, Miller, SD 57362	Miller	Miller, Miller	-98.989100	44.515800	NaN	0	Dollar($)	No	No	1	3.4	Orange	Average	11	United States

		Count
Country_Code	Country
1	India	8651
216	United States	434
215	United Kingdom	80
30	Brazil	60
189	South Africa	60
214	UAE	60
148	New Zealand	40
208	Turkey	34
14	Australia	24
162	Phillipines	22
94	Indonesia	21
166	Qatar	20
184	Singapore	20
191	Sri Lanka	20
37	Canada	4

	Count
count	141.000000
mean	67.730496
std	476.723952
min	1.000000
25%	1.000000
50%	20.000000
75%	20.000000
max	5473.000000

Identifying and Recommending Best Restaurants¶

impoprt Necessariess Lobararies¶

EDA-1 Explore the geographical distribution of the restaurants. Finding out the cities with maximum / minimum number of restaurants¶

EDA-2 Explore how ratings are distributed overall Evaluate the Highest Rated and Lowest Rated Restaurant of the City in all the countries.¶

EDA - 3 Ratio between restaurants that allow table booking vs that do not allow table booking. Percentage of restaurants providing online delivery. Difference in no. of votes for the restaurants that deliver and the restaurant that don’t.¶

EDA - 4 What are the top 10 cuisines served across cities? What is the maximum and minimum no. of cuisines that a restaurant serves?¶

We see that there is no single variable that affects the rating strongly, however table booking,online delivery,avg price for two and price range, number of votes do play a part in affecting the rating of a restaurant.¶

	Country_x	City	Restaurant_Name_x	Aggregate_rating_x	Country_y	Restaurant_Name_y	Aggregate_rating_y
0	Australia	Armidale	Whitebull Hotel	3.5	Australia	Whitebull Hotel	3.5
1	Australia	Balingup	Taste of Balingup	3.2	Australia	Taste of Balingup	3.2
2	Australia	Beechworth	Bridge Road Brewers	4.6	Australia	Bridge Road Brewers	4.6
3	Australia	Dicky Beach	The Giggling Goat	3.6	Australia	The Giggling Goat	3.6
4	Australia	East Ballina	The Belle General	4.1	Australia	The Belle General	4.1
...	...	...	...	...	...	...	...
136	United States	Valdosta	Smok'n Pig B-B-Q	4.1	United States	El Toreo Mexican Restaurant	3.1
137	United States	Vernonia	Blue House Cafe	4.3	United States	Blue House Cafe	4.3
138	United States	Waterloo	Four Queens Dairy Cream	3.9	United States	Masala Grill & Coffee House	3.2
139	United States	Weirton	Theo Yianni's Authentic Greek Restaurant	3.9	United States	Theo Yianni's Authentic Greek Restaurant	3.9
140	United States	Winchester Bay	Fishpatrick's Crabby Cafe	3.2	United States	Fishpatrick's Crabby Cafe	3.2

	Cuisine_1	Cuisine_2	Cuisine_3	Cuisine_4	Cuisine_5	Cuisine_6	Cuisine_7	Cuisine_8
9545	Chinese	North Indian	Fast Food	NaN	NaN	NaN	NaN	NaN
9546	Indian	Chinese	Continental	NaN	NaN	NaN	NaN	NaN
9547	Cafe	Continental	Desserts	Ice Cream	Italian	Beverages	NaN	NaN
9548	Street Food	NaN	NaN	NaN	NaN	NaN	NaN	NaN
9549	Chinese	North Indian	NaN	NaN	NaN	NaN	NaN	NaN

	Locality_Verbose	Cuisines	Count
0	ILD Trade Centre Mall, Sohna Road, Gurgaon	Cafe	1
1	ILD Trade Centre Mall, Sohna Road, Gurgaon	North Indian	1
2	ILD Trade Centre Mall, Sohna Road, Gurgaon	Beverages	1
3	ILD Trade Centre Mall, Sohna Road, Gurgaon	Mughlai	1
4	12th Square Building, Banjara Hills, Hyderabad	Mughlai	1

	Country	City	Locality	Cuisines	Number of restaurants in the country
3	India	New Delhi	Connaught Place	North Indian	48
14	United States	Dubuque	Dubuque	American	9
5	New Zealand	Wellington City	Te Aro	Cafe	5
1	Brazil	Rio de Janeiro	Ipanema	Brazilian	3
6	Phillipines	Mandaluyong City	SM Megamall, Ortigas, Mandaluyong City	Japanese	2
8	Singapore	Singapore	Marina Centre, Downtown Core	Seafood	2
9	South Africa	Cape Town	Green Point	Grill	2
11	Turkey	Ankara	Gazi Osman PaÅÙa	World Cuisine	2
12	UAE	Abu Dhabi	Abu Dhabi Mall, Tourist Club Area (Al Zahiyah)	American	2
0	Australia	Victor Harbor	Victor Harbor	Coffee and Tea	1
2	Canada	Vineland Station	Vineland Station	Italian	1
4	Indonesia	Jakarta	Tebet	Western	1
7	Qatar	Doha	The Westin Doha Hotel & Spa, Fereej Bin Mahmoud	Thai	1

	Restaurant_Name	Cuisines	Count
0	12212	Fast Food	1
1	Let's Burrrp	Chinese	1
2	Let's Burrrp	North Indian	1
3	#45	Cafe	1
4	#Dilliwaala6	North Indian	1
5	#InstaFreeze	Ice Cream	1
6	#OFF Campus	Cafe	1
7	#OFF Campus	Continental	1
8	#OFF Campus	Italian	1
9	#OFF Campus	Fast Food	1
10	#Urban CafÌ©	North Indian	1
11	#Urban CafÌ©	Chinese	1
12	#Urban CafÌ©	Italian	1
13	#hashtag	Cafe	1
14	'Ohana	Hawaiian	1
15	10 Downing Street	North Indian	2
16	10 Downing Street	Chinese	2
17	10 To 10 In Delhi	Indian	1
18	10 To 10 In Delhi	Cafe	1
19	11th Avenue Cafe Bistro	Cafe	1

	Restaurant_Name	Cuisines	Count
2479	Cafe Coffee Day	Cafe	83
4596	Domino's Pizza	Pizza	79
4597	Domino's Pizza	Fast Food	78
12984	Subway	Salad	63
12985	Subway	Healthy Food	63
...	...	...	...
5568	Gabbar's Bar & Kitchen	Chinese	1
5569	Gabbar's Bar & Kitchen	Mexican	1
5570	Gabbar's Bar & Kitchen	Italian	1
5571	Gaga Manjero	World Cuisine	1
15963	Ìàukura€Ùa Sofras€±	Izgara	1

	Average_Cost_for_two	Aggregate_rating
Average_Cost_for_two	1.00000	0.05011
Aggregate_rating	0.05011	1.00000

	Has_Table_booking_Yes	Aggregate_rating
Has_Table_booking_Yes	1.000000	0.181843
Aggregate_rating	0.181843	1.000000

		Count
Country	City
Australia	Armidale	1
	Balingup	1
	Beechworth	1
	Dicky Beach	1
	East Ballina	1
	Flaxton	1
	Forrest	1
	Huskisson	1
	Inverloch	1
	Lakes Entrance	1
	Lorn	1
	Macedon	1
	Mayfield	1
	Middleton Beach	1
	Montville	1
	Palm Cove	1
	Paynesville	1
	Penola	1
	Phillip Island	1
	Tanunda	1
	Trentham East	1
	Victor Harbor	1
Canada	Chatham-Kent	1
	Consort	1
	Vineland Station	1
	Yorkton	1
India	Mohali	1
India	Panchkula	1
Indonesia	Bandung	1
Phillipines	Quezon City	1
Phillipines	Tagaytay City	1
South Africa	Randburg	1
United States	Clatskanie	1
	Cochrane	1
	Fernley	1
	Lakeview	1
	Lincoln	1
	Mc Millan	1
	Miller	1